class_docstr: predictor configure
  :param pre_processor preprocessor of ImageSet before model inference
  :param post_processor postprocessor of ImageSet after model inference
  :param batch_per_partition batch size per partition
  :param label_map mapping from prediction result indexes to real dataset labels
  :param feature_padding_param featurePaddingParam if the inputs have variant size
code: def __init__(
      self,
      pre_processor=None,
      post_processor=None,
      batch_per_partition=4,
      label_map=None,
      feature_padding_param=None,
      jvalue=None,
      bigdl_type="float",
  ):
      self.bigdl_type = bigdl_type
      if jvalue:
          self.value = jvalue
      else:
          if pre_processor:
              assert issubclass(
                  pre_processor.__class__, Preprocessing
              ), "the pre_processor should be subclass of Preprocessing"
          if post_processor:
              assert issubclass(
                  post_processor.__class__, Preprocessing
              ), "the post_processor should be subclass of Preprocessing"
          self.value = callZooFunc(
              bigdl_type,
              JavaValue.jvm_class_constructor(self),
              pre_processor,
              post_processor,
              batch_per_partition,
              label_map,
              feature_padding_param,
          )
  
file_path: analytics-zoo/pyzoo/zoo/models/image/common/image_config.py
imports: import sys
  from bigdl.util.common import JavaValue
  from zoo.common.utils import callZooFunc
  from zoo.feature.common import Preprocessing
label_desc: Use image node_id 1 to create a new ImageConfigure object from inherited base classes: JavaValue with example: obj = ImageConfigure(pre_processor, post_processor, batch_per_partition, label_map, feature_padding_param, jvalue, bigdl_type)